This might explain the success of model-based techniques, where the neural network architectures are established by unfolding known decoding algorithms and where the weights are initialized based on these algorithms [1]}. Learning is then used to explore the functions in the function class of the neural network that are not “too far” from the initial point in the optimization space. Nevertheless, the initial point should already be of good quality to get satisfactory performance and learning amounts to fine tuning the algorithm.
